Nakul Verma

LECTURER IN DISCIPLINE OF COMPUTER SCIENCE

Nakul Verma studies machine learning and high-dimensional statistics. He focuses on understanding and exploiting the intrinsic structure in data to design effective learning algorithms. His work has produced the first provably correct approximate distance-preserving embeddings for manifolds from finite samples, and has provided improved sample complexity results in various learning paradigms, such as metric learning and multiple-instance learning.

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Prior to joining Columbia, Verma worked at the Janelia Research Campus of the Howard Hughes Medical Institute as a research specialist developing statistical techniques to analyze neuroscience data, where he collaborated with neuroscientists to quantitatively analyze social behavior in model organisms using various unsupervised and weakly-supervised machine learning techniques. Verma has also worked at Amazon as a research scientist developing risk assessment models for real-time fraud detection.

Verma received his PhD in 2012 and his BS in 2004, both from the University of California, San Diego.

He was awarded Provost Honors from University of California, San Diego from 2001-2004. He was awarded a Janelia Teaching Fellowship in 2015.